arXiv subjects

Ben DalFavero

Publications and source records attributed to Ben DalFavero.

6 recordsLinked to original sources

arXiv2026

Satisfying Quantum Codes: Physics-Informed and Hardware-Aware Code Design with SAT Solvers

Although quantum error correction is widely believed to be necessary for impactful applications of quantum computers, the design of quantum error correction codes is largely done by hand, without respect to problem or hardware constraints. In this work, we present a highly general and flexible framework for the computational design of both physics-inspired and hardware-aware quantum codes. To do so, we formulate code design as a Boolean satisfiability (SAT) problem and show how to incorporate all required error correction criteria. We prove that code design is NP-complete, ruling out any efficient algorithm for designing codes in general. Nonetheless, we show that state-of-the-art SAT solvers are able to effectively find solutions for many practical problems. Notably, we are able to design physics-inspired codes with up to 100 physical qubits in minutes, and we design new hardware-aware codes for biased noise which have a lower logical error rate than state-of-the-art surface codes.

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arXiv2026

The cost of quantum algorithms for biochemistry: A case study in metaphosphate hydrolysis

We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular signaling, and cancer therapeutics. In particular, we consider three algorithms for solving the ground state energy estimation problem: the variational quantum eigensolver, quantum Krylov, and quantum phase estimation. By utilizing exact classical simulation, numerical estimation, and analytical bounds, we provide a current and future outlook for using quantum computers to solve impactful biochemical and biological problems. Our results show that variational methods, while being the most heuristic, still require substantially fewer overall resources on quantum hardware, and could feasibly address such problems on current or near-future devices. We include our complete dataset of biomolecular Hamiltonians and code as benchmarks to improve upon with future techniques.

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arXiv2025

Error mitigation for partially error-corrected quantum computers

We present a method for quantum error mitigation on partially error-corrected quantum computers - i.e., computers with some logical qubits and some noisy qubits. Our method is inspired by the error cancellation method and is implemented via a circuit for convex combinations of channels which we introduce in this work. We show how logical ancilla qubits can arbitrarily reduce the sampling complexity of error cancellation in a continuous space-time tradeoff, in the limiting case achieving $O(1)$ sample complexity which circumvents lower bounds for sample complexity with all known error mitigation techniques. This comes at the cost of exponential circuit depth, however, and leads us to conjecture that any error mitigation protocol with (sub-)polynomial sample complexity requires exponential time and/or space, even when logical qubits are utilized as a resource. We anticipate additional applications for our quantum circuits to implement convex combinations of channels, and to this end we discuss one application in simulating open quantum systems, showing an order of magnitude reduction in gate counts relative to current state-of-the-art methods for a canonical problem.

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arXiv2025

Characterizing Superconducting Qubits using Averaged Circuit Eigenvalue Sampling

Efficient characterization of noise during quantum gate operations is an essential step to building and scaling up a quantum computer. One such protocol is averaged circuit eigenvalue sampling (ACES) which efficiently characterizes a noisy gate set by reconstructing a Pauli noise model for a each gate. Here we utilize the ACES protocol to characterize two coupled superconducting qubits. For accurate reconstruction, we tailor the noise via Pauli twirling and account for measurement errors. We verify the accuracy of the protocol by comparing the predicted gate fidelities to that extracted from conventional benchmarking approaches, such as interleaved randomized benchmarking. Furthermore, we demonstrate the efficacy of ACES in accurately identifying specific noise sources by reconstructing injected phase errors in the two-qubit gates.

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arXiv2025

Measurement reduction for expectation values via fine-grained commutativity

We introduce a notion of commutativity between operators on a tensor product space, nominally Pauli strings on qubits, that interpolates between qubit-wise commutativity and (full) commutativity. We apply this notion, which we call $k$-commutativity, to measuring expectation values of observables in quantum circuits and show a reduction in the number measurements at the cost of increased circuit depth. Last, we discuss the asymptotic measurement complexity of $k$-commutativity for several families of $n$-qubit Hamiltonians, showing examples with $O(1)$, $O(\sqrt{n})$, and $O(n)$ scaling.

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arXiv2025

Non-Clifford diagonalization for measurement shot reduction in quantum expectation value estimation

Estimating expectation values on near-term quantum computers often requires a prohibitively large number of measurements. One widely-used strategy to mitigate this problem has been to partition an operator's Pauli terms into sets of mutually commuting operators. Here, we introduce a method that relaxes this constraint of commutativity, instead allowing for entirely arbitrary terms to be grouped together, save a locality constraint. The key idea is that we decompose the operator into arbitrary tensor products with bounded tensor size, ignoring Pauli commuting relations. This method -- named $k$-NoCliD ($k$-local non-Clifford diagonalization) -- allows one to measure in far fewer bases in most cases, often (though not always) at the cost of increasing the circuit depth. We introduce several partitioning algorithms tailored to different Hamiltonian classes. For electronic structure, we numerically demonstrate the existence of threshold values of $k$ for which $k$-NoCliD leads to the lowest shot counts, though we leave improved partitioning algorithms to future work. We focus primarily on three Hamiltonian classes -- molecular vibrational structure, Fermi-Hubbard, and Bose-Hubbard -- and show that $k$-NoCliD reduces the number of circuit shots, often by a very large margin, and often even for $k$ as small as 2.

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