What we mean by "practical" quantum algorithms
Near-term quantum devices have limited qubit counts and are affected by noise. Our goal is not only to propose algorithms, but also to make them implementable and measurable under realistic constraints: gate sets, connectivity, depth budgets, and measurement cost.
Compilation & circuit synthesis
Quantum compilation translates an abstract circuit into a form that can actually run on a specific device. This includes gate decomposition, qubit routing under limited connectivity, and circuit simplification.
Device-aware routing
Map logical qubits onto physical qubits, insert SWAPs efficiently, and keep two-qubit gates minimal.
Depth & error budgeting
Reduce depth and entangling gates, because they dominate error on many platforms.
Unitary / gate synthesis
For targets like Toffoli or multi-qubit primitives, we study synthesis strategies that minimize resources while keeping gradients reliable for optimization-based compilation.
QAOA for combinatorial optimization
QAOA (Quantum Approximate Optimization Algorithm) is a leading approach for near-term optimization. We study how to make QAOA work reliably in practice: choosing cost/mixer design, improving trainability, and understanding performance under finite depth and noise.
Problem-aware design
Encode structure (constraints, symmetry, hardware limits) into mixers and cost Hamiltonians.
Trainability
Diagnose optimization difficulty, use schedules/initializations that converge faster.
VQE & ground-state preparation
VQE (Variational Quantum Eigensolver) targets ground states of Hamiltonians important in simulation and chemistry. Our focus is on ansatz design, gradient stability, and measurement efficiency.
What we optimize
- Ansatz expressibility vs depth (avoid over-parameterized circuits that become noisy)
- Gradient reliability (cross-check PSR/estimators when needed)
- Measurement cost (grouping, shot allocation, and variance control)
FALQON & feedback-based control
FALQON is a feedback-based quantum algorithm where parameter updates are guided by measurable quantities, reducing reliance on heavy classical outer-loop training. We explore how feedback can improve robustness, and how to hybridize feedback with adaptive schedules.
How we evaluate performance
We emphasize metrics that matter on real devices, not only ideal simulations.
Resource metrics
Depth, two-qubit gate count, routing overhead, and measurement cost.
Robustness metrics
Sensitivity to noise, calibration errors, and reproducibility across runs.
Where it can be used
Our algorithm + compilation pipelines support research and prototyping across multiple directions: optimization, simulation, and hardware benchmarking.
FAQ
Do you focus more on theory or implementation?
Both. We start from clear algorithmic goals, then design compilation and evaluation so the ideas can be tested on realistic devices and simulator baselines.
What problems are a good fit for QAOA / VQE?
QAOA is often used for combinatorial objectives (graph and constraint-based problems). VQE targets Hamiltonian ground states and simulation tasks.
Can I join as a student or visiting researcher?
Yes. If you have interest in quantum software, optimization, or experimental constraints, we welcome collaborations.
Selected reading
- Algorithm–compiler co-design for near-term devices
- Verified gradient methods for circuit synthesis
- Feedback-based quantum control and FALQON-style updates