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Found 24 papers, 10 papers with code
The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits
We address these challenges with the stabilizer bootstrap, a method that uses stabilizer-based techniques to optimize quantum neural networks before their quantum execution, together with theoretical proofs and high-performance computing with 10000 qubits or random datasets up to 1000 data.
Quantum-machine-assisted Drug Discovery: Survey and Perspective
Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market.
Deep Learning for Low-Latency, Quantum-Ready RF Sensing
Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals.
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models.
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP).
DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting
By counting the occurrences of edges and edge pairs in decoded matchings, we can statistically estimate the up-to-date probabilities of each edge and the correlations between them.
Training Quantum Boltzmann Machines with Coresets
Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices.
Fundamental causal bounds of quantum random access memories
Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tasks such as linear algebra, data search, and machine learning, is often proposed to offer $\mathcal{O}(\log N)$ circuit depth for $\mathcal{O}(N)$ data size, given $N$ qubits.
Spacetime-Efficient Low-Depth Quantum State Preparation with Applications
When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an $N$-dimensional state in depth $O(\log(N))$ and spacetime allocation (a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) $O(N)$, which are both optimal.
SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$.
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware.
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines.
QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis
Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity.
Quantum Physics
Systematic Crosstalk Mitigation for Superconducting Qubits via Frequency-Aware Compilation
One of the key challenges in current Noisy Intermediate-Scale Quantum (NISQ) computers is to control a quantum system with high-fidelity quantum gates.
Quantum Physics
Coreset Clustering on Small Quantum Computers
However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms.
Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse
Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set.
Quantum Physics Systems and Control Systems and Control
SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via Cost-Effective Uncomputation
Compiling high-level quantum programs to machines that are size constrained (i. e. limited number of quantum bits) and time constrained (i. e. limited number of quantum operations) is challenging.
Quantum Physics
Term Grouping and Travelling Salesperson for Digital Quantum Simulation
Digital simulation of quantum dynamics by evaluating the time evolution of a Hamiltonian is the initially proposed application of quantum computing.
Quantum Physics
Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families
Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum machines.
Quantum Physics
Formal Constraint-based Compilation for Noisy Intermediate-Scale Quantum Systems
For large programs and machine sizes, the SMT optimization approach can be used to synthesize compiled code that is guaranteed to finish within the coherence window of the machine.
Programming Languages Quantum Physics
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
A massive gap exists between current quantum computing (QC) prototypes, and the size and scale required for many proposed QC algorithms.
Quantum Physics Programming Languages
Optimized Surface Code Communication in Superconducting Quantum Computers
Quantum computing (QC) is at the cusp of a revolution.
Quantum Physics
ScaffCC: Scalable Compilation and Analysis of Quantum Programs
We present ScaffCC, a scalable compilation and analysis framework based on LLVM, which can be used for compiling quantum computing applications at the logical level.
Quantum Physics Programming Languages
