arXiv subjects

Gregory Boyd

Publications and source records attributed to Gregory Boyd.

6 recordsLinked to original sources

arXiv2026

Transmon Phase Gates Controlled by Superconducting Soliton DAC

We introduce a superconducting digital-to-analog converter (DAC) that filters control noise, provides native multiplexing, performs quantum gates in nanoseconds, and can be controlled by CMOS. This is achieved by transducing a trapezoidal drive pulse into a superconducting soliton, which is then held in the DAC load loop, applying flux to a mutually-coupled superconducting qubit or gate coupler. The analog flux output by the DAC can be easily controlled by varying the soliton hold time, or with a DC-biased tunable DAC-qubit coupler, allowing the DAC to perform a fixed-time, high-fidelity gate that's robust to fabrication variance or flux offsets in the quantum circuit. Our initial demonstration shows that the DAC can successfully perform 5.6 ns S-gates on transmons. We measure the DAC-induced quantum state excitation probability per gate to be 0.05%, and find that the DAC-induced relaxation rate from the qubit 1 state is below the intrinsic T1 rate limit of the transmon. Quantum simulations show qualitative agreement with the measured data, and predict that the DAC excitation rate can be lowered 10 times further by overdamping the Josephson junction (JJ) in the DAC load loop. may be limited by a Interleaved Randomized Benchmarking (IRB) sequences on an observer qubit reveal that, when scaling to many qubits, the DAC's performance may be limited by a non-local, DAC-induced phase error of 1.6% per gate, appearing in ancilla qubits that are not directly coupled to any of the 30 DACs on the chip. We discuss strategies for future layouts of multi-DAC chips that focus on mitigating the source of these non-local, high-frequency electromagnetic interactions (EMI), and how to incorporate a DC-tunable coupler for phase correction.

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arXiv2025

Fullqubit alchemist: Quantum algorithm for alchemical free energy calculations

Accurately computing the free energies of biological processes is a cornerstone of computer-aided drug design, but it is a daunting task. The need to sample vast conformational spaces and account for entropic contributions makes the estimation of binding free energies very expensive. While classical methods, such as thermodynamic integration and alchemical free energy calculations, have significantly contributed to reducing computational costs, they still face limitations in terms of efficiency and scalability. We tackle this through a quantum algorithm for the estimation of free energy differences by adapting the existing Liouvillian approach and introducing several key algorithmic improvements. We directly implement the Liouvillian operator and provide an efficient description of electronic forces acting on both nuclear and electronic particles on the quantum ground state potential energy surface. This leads to super-polynomial runtime scaling improvements in the precision of our Liouvillian simulation approach and quadratic improvements in the scaling with the number of particles relative to prior quantum algorithms. Second, our algorithm calculates free energy differences via a fully quantum implementation of thermodynamic integration and alchemy, thereby foregoing expensive entropy estimation subroutines used in prior works. Our results open new avenues towards the application of quantum computers in drug discovery.

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arXiv2024

Low Depth Phase Oracle Using a Parallel Piecewise Circuit

We explore the important task of applying a phase $\exp(i\,f(x))$ to a computational basis state $\left| x \right>$. The closely related task of rotating a target qubit by an angle depending on $f(x)$ is also studied. Such operations are key in many quantum subroutines, and frequently $f(x)$ can be well-approximated by a piecewise function; examples range from the application of diagonal Hamiltonian terms (such as the Coulomb interaction) in grid-based many-body simulation, to derivative pricing algorithms. Here we exploit a parallelisation of the piecewise approach so that all constituent elementary rotations are performed simultaneously, that is, we achieve a total rotation depth of one. Moreover, we explore the use of recursive catalyst `towers' to implement these elementary rotations efficiently. We find that strategies prioritising execution speed can achieve circuit depth as low as $O(\log{n}{+}\log{S})$ for a register of $n$ qubits and a piecewise approximation of $S$ sections (presuming prior preparation of enabling resource states), albeit total qubit count then scales with $S$. In the limit of multiple repetitions of the oracle, we find that catalyst tower approaches have an $O(S\cdot n)$ T-count.

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arXiv2024

High-Dimensional Subspace Expansion Using Classical Shadows

We introduce a post-processing technique for classical shadow measurement data that enhances the precision of ground state estimation through high-dimensional subspace expansion; the dimensionality is only limited by the amount of classical post-processing resources rather than by quantum resources. Crucial steps of our approach are the efficient identification of useful observables from shadow data, followed by our regularised subspace expansion that is designed to be numerically stable even when using noisy data. We analytically investigate noise propagation within our method, and upper bound the statistical fluctuations due to the limited number of snapshots in classical shadows. In numerical simulations, our method can achieve a reduction in the energy estimation errors in many cases, sometimes by more than an order of magnitude. We also demonstrate that our performance improvements are robust against both coherent errors (bad initial state) and gate noise in the state-preparation circuits. Furthermore, performance is guaranteed to be at least as good - and in many cases better - than direct energy estimation without using additional quantum resources and the approach is thus a very natural alternative for estimating ground state energies directly from classical shadow data.

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arXiv2023

Low-Overhead Parallelisation of LCU via Commuting Operators

The Linear Combination of Unitaries (LCU) method is a powerful scheme for the block encoding of operators but suffers from high overheads. In this work, we discuss the parallelisation of LCU and in particular the SELECT subroutine of LCU based on partitioning of observables into groups of commuting operators, as well as the use of adaptive circuits and teleportation that allow us to perform required Clifford circuits in constant depth. We additionally discuss the parallelisation of QROM circuits which are a special case of our main results, and provide methods to parallelise the action of multi-controlled gates on the control register. We only require an $O(\log n)$ factor increase in the number of qubits in order to produce a significant depth reduction, with prior work suggesting that for molecular Hamiltonians, the depth saving is $O(n)$, and numerics indicating depth savings of a factor approximately $n/2$. The implications of our method in the fault-tolerant setting are also considered, noting that parallelisation reduces the $T$-depth by the same factor as the logical algorithm, without changing the $T$-count, and that our method can significantly reduce the overall space-time volume of the computation, even when including the increased number of $T$ factories required by parallelisation.

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arXiv2022

Training variational quantum circuits with CoVaR: covariance root finding with classical shadows

Exploiting near-term quantum computers and achieving practical value is a considerable and exciting challenge. Most prominent candidates as variational algorithms typically aim to find the ground state of a Hamiltonian by minimising a single classical (energy) surface which is sampled from by a quantum computer. Here we introduce a method we call CoVaR, an alternative means to exploit the power of variational circuits: We find eigenstates by finding joint roots of a polynomially growing number of properties of the quantum state as covariance functions between the Hamiltonian and an operator pool of our choice. The most remarkable feature of our CoVaR approach is that it allows us to fully exploit the extremely powerful classical shadow techniques, i.e., we simultaneously estimate a very large number $>10^4-10^7$ of covariances. We randomly select covariances and estimate analytical derivatives at each iteration applying a stochastic Levenberg-Marquardt step via a large but tractable linear system of equations that we solve with a classical computer. We prove that the cost in quantum resources per iteration is comparable to a standard gradient estimation, however, we observe in numerical simulations a very significant improvement by many orders of magnitude in convergence speed. CoVaR is directly analogous to stochastic gradient-based optimisations of paramount importance to classical machine learning while we also offload significant but tractable work onto the classical processor.

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