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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantum Entanglement between Optical and Microwave Photonic Qubits

Entanglement is an extraordinary feature of quantum mechanics. Sources of entangled optical photons were essential to test the foundations of quantum physics through violations of Bell’s inequalities. More recently, entangled many-body states have been realized via strong nonlinear interactions in microwave circuits with superconducting qubits. Here, we demonstrate a chip-scale source of entangled optical and microwave photonic qubits. Our device platform integrates a piezo-optomechanical transducer with a superconducting resonator which is robust under optical illumination. We drive a photon-pair generation process and employ a dual-rail encoding intrinsic to our system to prepare entangled states of microwave and optical photons. We place a lower bound on the fidelity of the entangled state by measuring microwave and optical photons in two orthogonal bases. This entanglement source can directly interface telecom wavelength time-bin qubits and gigahertz frequency superconducting qubits, two well-established platforms for quantum communication and computation, respectively. Published by the American Physical Society 2024

Physics↗
DOE OSTI2026

Sapphire Substrate Trenching to Advance Superconducting High-Coherence Quantum Devices

Achieving longer coherence times in superconducting qubits is essential for advancing quantum information processing and enabling scalable quantum computing. This work presents an innovative fabrication strategy focused on sapphire trenching of the substrate of superconducting high-coherence quantum devices, developed by the SQMS Nanofabrication Taskforce in collaboration with the materials characterization team. In this talk, we present a method to efficiently and consistently trench into the sapphire substrate, without compromising the surface roughness or profile of the etched sapphire. In the literature, trenching in the substrate has been shown to decrease losses, unwanted coupling and thermal stress. Sapphire’s strong covalent bonds make it harder to etch compared to silicon, yet it is more favorable for high-coherence quantum devices due to its lower dielectric losses. This innovative technique opens new possibilities for superconducting qubit fabrication: we fabricated high coherence qubit chips on sapphire substrates with trenching, to demonstrate that it will help pushing towards higher coherence times. By integrating this work with ongoing junction process optimization, design innovation, materials characterization and exploration, we outline a clear pathway toward transmon coherence times reaching millisecond scales and beyond.

Garattoni, S. [Fermilab]↗
DOE OSTI2026

Sapphire Substrate Trenching to Advance Superconducting High-Coherence Quantum Devices

Achieving longer coherence times in superconducting qubits is essential for advancing quantum information processing and enabling scalable quantum computing. This work presents an innovative fabrication strategy focused on sapphire trenching of the substrate of superconducting high-coherence quantum devices, developed by the SQMS Nanofabrication Taskforce in collaboration with the materials characterization team. In this talk, we present a method to efficiently and consistently trench into the sapphire substrate, without compromising the surface roughness or profile of the etched sapphire. In the literature, trenching in the substrate has been shown to decrease losses, unwanted coupling and thermal stress. Sapphire’s strong covalent bonds make it harder to etch compared to silicon, yet it is more favorable for high-coherence quantum devices due to its lower dielectric losses. This innovative technique opens new possibilities for superconducting qubit fabrication: we fabricated high coherence qubit chips on sapphire substrates with trenching, to demonstrate that it will help pushing towards higher coherence times. By integrating this work with ongoing junction process optimization, design innovation, materials characterization and exploration, we outline a clear pathway toward transmon coherence times reaching millisecond scales and beyond.

Garattoni, S. [Fermilab]↗
DOE OSTI2026

A Cryogenic Muon Tagging System Integrated with a Superconducting Qubit Device for Radiation-Induced Error Mitigation

Superconducting qubits are highly sensitive to ionizing radiation, which can induce correlated errors and limit scalable fault-tolerant quantum computing. In particular, cosmic-ray muons can deposit energy in the substrate, generating phonon bursts that break Cooper pairs and produce quasiparticles, leading to correlated decoherence events across multiple qubits. We present the development of a cryogenic muon tagging system based on Kinetic Inductance Detectors (KIDs) and its integration with superconducting quantum hardware. Originally developed within the ACE-SuperQ project and validated as a standalone detector, the system demonstrated a muon tagging efficiency of approximately 90% and excellent agreement with Monte Carlo simulations. Building on this validation, the tagging system has been integrated with a multi-qubit superconducting chip operated in a dilution refrigerator. The detector configuration consists of a multi-layer KID stack arranged above and below the quantum device, enabling time-coincident identification of muon-induced events within the same cryogenic environment. The integrated setup has been successfully commissioned, enabling simultaneous operation of the qubit chip and the muon tagging system. A first measurement campaign has been carried out, and preliminary data show time-correlated events between the muon tagging detectors and the qubit readout. A quantitative analysis of radiation-induced effects on qubit performance is currently ongoing. This work represents a step toward the implementation of event-level radiation tagging as a tool for characterizing and potentially mitigating correlated errors in superconducting quantum processors, while establishing a modular platform for future studies at the interface between particle physics and quantum information science.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2025

Spin Dynamics of SCRPs in ZnO Quantum Dot–Organic Molecule Conjugates Studied with Pulsed EPR

Photogenerated spin-correlated radical pairs (SCRPs) are emerging as promising new candidates for Quantum Information Science applications, where they act as electron spin qubit pairs (SQPs). Historically, these pairs have been mostly investigated in natural photosynthesis and in molecular organic donor–(linker)–acceptor systems. Recently, we have shown that these spin pairs can also be observed by time-resolved electron-paramagnetic resonance (EPR) spectroscopy in hybrid inorganic–organic conjugates. The current study builds on recent work (ACS Nano, 2025, 19, 12194–12207), in which we systematically prepared hybrid inorganic–organic molecule systems with tunable geometries that can host SCRPs/SQPs. Here, we demonstrate using pulsed EPR spectroscopy that we can generate, study, and manipulate the spins in these photogenerated SCRPs hosted on highly tailorable inorganic–organic hybrid systems. Microwave pulse-based spin manipulation is the first step toward developing quantum computing and quantum sensing applications with these promising spin-based qubit materials, which can be reversibly photogenerated in highly spin-polarized states at moderate temperatures. An attractive feature of this new class of hybrid materials is that the g-factor of the unpaired electron spin in the ZnO QD (as part of the SCRP/SQP) can be adjusted using the quantum size effect in these QDs, which allows for the selective addressability of each spin in the SCRP using microwave pulses. Additionally, we also observed a difference between the g-values of the unpaired electron in the ZnO QD in the transient SCRP state and stable photoreduced state. We hypothesize that the unpaired electron in the SCRP is delocalized as a band-like conduction electron, while in the stable photoreduced state, the electron is delocalized in a shallow electron trap state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Logical shadow tomography: Efficient estimation of error-mitigated observables

We introduce a technique to estimate error-mitigated expectation values on noisy quantum computers. Our technique performs shadow tomography on a logical state to produce a memory-efficient classical reconstruction of the noisy density matrix. Using efficient classical post-processing, one can mitigate errors by projecting into the codespace as in subspace expansion and taking powers of the density matrix as in virtual distillation. Relative to subspace expansion which requires Ω (2^((n-1)k) samples to estimate a Pauli observable with an [[n; k]] stabilizer code, our technique requires only Ө(2^k) samples. Relative to virtual distillation, our technique can compute powers of the density matrix without implementing additional copies of quantum states the quantum computer. We present numerical results using logical states encoded with up to sixty physical qubits and show fast convergence to error-free expectation values with only 10^5 samples under 1% depolarizing noise.

quantum computing↗
DOE OSTI2026

Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation

Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic quantum information processing. The inverse design of such systems, i.e., recovering device geometries that produce specified electromagnetic and coupling targets, is generally a one-to-many problem. The qubit-cavity coupling strength depends sensitively on both the transmon geometry and its position within the cavity's electromagnetic field. As these systems scale up and their design parameter spaces grow, the cost of conventional iterative simulation becomes prohibitive. We present two deep neural network (DNN) approaches that address this inverse-design problem at complementary levels of the design stack. The first proposes SRF cavity geometries that produce target cavity observables. The second proposes transmon qubit designs that produce target qubit-cavity parameters - the coupling rate, qubit frequency, and anharmonicity $(g, ν_q, α)$. The recovered candidate designs match the targets to within ~5% (cavity) and ~2% (transmon), confirmed by end-to-end re-simulation. Both approaches map desired device behavior directly to candidate designs, a fast alternative to the iterative simulation studies usually required.

Yaker, Joseph [Fermilab; Northwestern U.]↗
DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

We introduce a self-consistent mean-field quantum optimization algorithm that approximates the ground state of classical Ising Hamiltonians. The algorithm decomposes the problem into independent subproblems and treats the interactions between them in a mean-field manner. These interactions are captured by a common environment, constructed self-consistently through a variational quantum circuit, and which modifies the subproblems to account for mutual influence while maintaining computational independence. Consequently, subproblems can be solved individually, avoiding the computational cost of the full problem. We explore the properties of the generated environment and assess the algorithm's performance through extensive numerical simulations on Sherrington-Kirkpatrick spin glasses. Furthermore, we apply it experimentally to a weighted maximum clique problem applied to molecular docking. This framework enables the solution of problems that would otherwise exceed the qubit and gate counts of current quantum hardware.

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗
DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

We introduce a self-consistent mean-field quantum optimization algorithm that approximates the ground state of classical Ising Hamiltonians. The algorithm decomposes the problem into independent subproblems and treats the interactions between them in a mean-field manner. These interactions are captured by a common environment, constructed self-consistently through a variational quantum circuit, and which modifies the subproblems to account for mutual influence while maintaining computational independence. Consequently, subproblems can be solved individually, avoiding the computational cost of the full problem. We explore the properties of the generated environment and assess the algorithm's performance through extensive numerical simulations on Sherrington-Kirkpatrick spin glasses. Furthermore, we apply it experimentally to a weighted maximum clique problem applied to molecular docking. This framework enables the solution of problems that would otherwise exceed the qubit and gate counts of current quantum hardware.

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗
DOE OSTI2026

Self-consistent mean-field quantum approximate optimization

We introduce a self-consistent mean-field quantum optimization algorithm that approximates the ground state of classical Ising Hamiltonians. The algorithm decomposes the problem into independent subproblems and treats the interactions between them in a mean-field manner. These interactions are captured by a common environment, constructed self-consistently through a variational quantum circuit, and which modifies the subproblems to account for mutual influence while maintaining computational independence. Consequently, subproblems can be solved individually, avoiding the computational cost of the full problem. We explore the properties of the generated environment and assess the algorithm's performance through extensive numerical simulations on Sherrington-Kirkpatrick spin glasses. Furthermore, we apply it experimentally to a weighted maximum clique problem applied to molecular docking. This framework enables the solution of problems that would otherwise exceed the qubit and gate counts of current quantum hardware.

Dupont, Maxime [Rigetti Computing] (ORCID:00000001↗
DOE OSTI2025

Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware

We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision. The network feedforward involves qubit rotations whose angles depend on the results of measurements in the previous layer. The network is trained via simulation, but inference is performed experimentally on quantum hardware. The classical-to-quantum correspondence is controlled by an interpolation parameter, $a$, which is zero in the classical limit. Increasing $a$ introduces quantum uncertainty into the measurements, which is shown to improve network performance at moderate values of the interpolation parameter. We then focus on particular images that fail to be classified by a classical neural network but are detected correctly in the quantum network. For such borderline cases, we observe strong deviations from the simulated behavior. We attribute this to physical noise, which causes the output to fluctuate between nearby minima of the classification energy landscape. Such strong sensitivity to physical noise is absent for clear images. We further benchmark physical noise by inserting additional single-qubit and two-qubit gate pairs into the neural network circuits. Our work provides a springboard toward more complex quantum neural networks on current devices: while the approach is rooted in standard classical machine learning, scaling up such networks may prove classically non-simulable and could offer a route to near-term quantum advantage.

FOS: Physical sciences↗
NASA NTRS2005

Scheme for Entering Binary Data Into a Quantum Computer

A quantum algorithm provides for the encoding of an exponentially large number of classical data bits by use of a smaller (polynomially large) number of quantum bits (qubits). The development of this algorithm was prompted by the need, heretofore not satisfied, for a means of entering real-world binary data into a quantum computer. The data format provided by this algorithm is suitable for subsequent ultrafast quantum processing of the entered data. Potential applications lie in disciplines (e.g., genomics) in which one needs to search for matches between parts of very long sequences of data. For example, the algorithm could be used to encode the N-bit-long human genome in only log2N qubits. The resulting log2N-qubit state could then be used for subsequent quantum data processing - for example, to perform rapid comparisons of sequences.

Williams, Colin↗
DOE OSTI2024

A dual-species Rydberg array

Large-scale Rydberg atom arrays are used for highly coherent analogue quantum simulations and for digital quantum computations. However, advanced quantum protocols, such as quantum error correction, require midcircuit qubit operations, including the replenishment, reset and read-out of a subset of qubits. A compelling strategy for unlocking these capabilities is a dual-species architecture in which a second atomic species is controlled independently and entangled with the first through Rydberg interactions. Here, we realize a dual-species Rydberg array consisting of rubidium and caesium atoms and explore regimes of interactions and dynamics not accessible in single-species architectures. We achieve enhanced interspecies interactions by electrically tuning the Rydberg states close to a Förster resonance. In this regime, we demonstrate an interspecies Rydberg blockade and implement a quantum state transfer from one species to another. We then generate a Bell state between Rb and Cs hyperfine qubits through an interspecies controlled-phase gate. Finally, we combine interspecies entanglement with a native midcircuit read-out to achieve quantum non-demolition measurements.

atomic and molecular interactions with photons↗
DOE OSTI2025

Mitigating cosmic-ray-like correlated events with a modular quantum processor

Quantum processors based on superconducting qubits are being scaled to larger qubit numbers, enabling the implementation of small-scale quantum error-correction codes. However, catastrophic chip-scale correlated errors have been observed in these processors, attributed to, e.g., cosmic ray impacts, which challenge conventional error-correction codes such as the surface code. These events are characterized by a temporary but pronounced suppression of the qubit-energy relaxation times. Here, in this study, we explore the potential for modular quantum computing architectures to mitigate such correlated energy decay events. We measure cosmic-ray-like events in a quantum processor comprising a motherboard and two flip-chip bonded daughterboard modules, each module containing two superconducting qubits. We monitor the appearance of correlated qubit decay events within a single module and across the physically separated modules. We find that while decay events within one module are strongly correlated (over 85%), events in separate modules only display approximately 2% correlations. We also report coincident decay events in the motherboard and in either of the two daughterboard modules, providing further insight into the nature of these decay events. These results suggest that modular architectures, combined with bespoke errorcorrection codes, offer a promising approach for protecting future quantum processors from chip-scale correlated errors.

Wu, Xuntao [Univ. of Chicago, IL (United States)] ↗
DOE OSTI2025

Low-latency quantum control using AI algorithms on cryogenic microelectronics platforms

Superconducting quantum computers are a particular technology that involve the realization of qubits by means of LC circuits with a Josephson junction. As shown in Fig. 1, this latter addition has the effect of introducing a nonlinearity (anharmonicity) that makes the frequency spacing between each energy level non-uniform. This is a desired effect for separating the two lowest levels, used for computation, from the higher ones, that are theoretically infinite and which are not desired to be used.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms

The computational capabilities of near-term quantum computers are limited by the noisy execution of gate operations and a limited number of physical qubits. Hybrid variational algorithms are well-suited to near-term quantum devices because they allow for a wide range of tradeoffs between the amount of quantum and classical resources used to solve a problem. This paper investigates tradeoffs available at both the algorithmic and hardware levels by studying a specific case – applying the Quantum Approximate Optimization Algorithm (QAOA) to instances of the Maximum Independent Set (MIS) problem. We consider three variants of the QAOA which offer different tradeoffs at the algorithmic level in terms of their required number of classical parameters, quantum gates, and iterations of classical optimization needed. Since MIS is a constrained combinatorial optimization problem, the QAOA must respect the problem constraints. This can be accomplished by using many multi-controlled gate operations which must be decomposed into gates executable by the target hardware. We study the tradeoffs available at this hardware level, combining the gate fidelities and decomposition efficiencies of different native gate sets into a single metric called the gate decomposition cost .

Tomesh, Teague↗
DOE OSTI2025

Improved Charge Sensing on a SiMOS Double Quantum Dot using a Cryogenic Skipper Readout ASIC (Quandarum)

Major outstanding questions in high-energy physics such as the nature of dark matter and the existence of interactions beyond the standard model require new measurement techniques which are extremely sensitive to minute electromagnetic fields. An array of entangled spin qubits is a promising system for building novel detectors due to its combination of sensitivity and controllability. CMOS-based electron spin qubits, which have demonstrated the operational requirements for fault-tolerant quantum computing [1], offer a particular opportunity due to their compatibility with classical electronics, which allows the leveraging of decades of development of low-noise cryogenic detectors for physics. In this work, we combine a SiMOS double-quantum dot device architecture with a state-of-the-art cryoelectronic readout circuit [2-3] aimed to demonstrate improved charge readout using a single-electron transistor (SET). We identify the design characteristics for an SET that facilitate the use of on-chip classical electronics as a low-power, high-bandwidth first amplification stage and explore opportunities for sensor-readout co-design to minimize noise. This is the first of a series of steps to demonstrate high-fidelity readout of a large array of spin qubit with enough sensitivity to probe processes of interest for the investigation of beyond-standard-model physics.

Quinn, Adam [Fermilab]↗