Presear researches and develops quantum AI solutions — variational quantum circuits, quantum kernels, hybrid classical-quantum algorithms, and quantum optimisation — for problems beyond classical computational reach.
Technical Depth
From variational algorithms to hybrid pipelines — quantum AI approaches matched to the problems that benefit most from quantum advantage.
Using parameterised quantum circuits optimised by classical gradient methods to find ground-state energies of molecular Hamiltonians — the key computational challenge in drug discovery and materials science. VQE is among the most promising near-term quantum algorithms for chemistry simulation, outperforming classical methods on specific molecular systems.
Applying quantum circuits to combinatorial optimisation problems — portfolio selection, route optimisation, network design, and scheduling — where the solution space is too vast for classical exhaustive search. QAOA encodes constraints as quantum Hamiltonians and finds approximate optimal solutions with potential polynomial speedups.
Mapping classical data into exponentially large Hilbert spaces using quantum feature maps — creating kernels for SVM classification that may be hard for classical computers to compute efficiently. Quantum kernels offer potential advantages for specific structured datasets, particularly in genomics, financial time series, and materials property prediction.
Designing variational quantum circuits as trainable neural network layers — with quantum data encoding, parameterised rotations, and measurement as the output layer. QNNs are trained using gradient-based optimisation (parameter shift rule) and can be composed with classical neural layers in hybrid architectures for practical ML tasks.
Leveraging D-Wave quantum annealers to solve QUBO (Quadratic Unconstrained Binary Optimisation) problems — logistics scheduling, financial portfolio optimisation, traffic routing, and network design — by physically evolving toward minimum energy configurations that correspond to optimal solutions.
Architecting systems where quantum processors handle the computationally hard subroutines while classical computers manage data processing, optimiser loops, and result post-processing. Hybrid pipelines are the practical path to quantum advantage today — using quantum hardware for targeted tasks where it provides genuine speedup over classical alternatives.
How We Work
A rigorous five-stage process — from suitability assessment to hybrid deployment and benchmarking — for quantum AI engagements.
Step 01 — Problem Suitability Assessment
Not every problem benefits from quantum computing — and we say so honestly. We map your problem structure to known quantum advantage regimes: combinatorial optimisation, molecular simulation, linear systems, and kernel methods — then quantify the expected speedup against best classical benchmarks.
Step 02 — Quantum Algorithm Design
We translate the business problem into a quantum-compatible formulation — QUBO for annealing, Hamiltonian for VQE/QAOA, or kernel definition for QSVMs. Algorithm selection balances the target advantage against noise sensitivity, qubit requirements, and circuit depth achievable on near-term hardware.
Step 03 — Circuit Implementation & Simulation
Circuits are implemented in Qiskit or PennyLane and first validated on state-vector simulators for correctness, then on noisy simulators to characterise hardware error sensitivity. Classical simulation allows rapid iteration before spending quantum hardware time on circuit validation.
Step 04 — Quantum Hardware Testing
Validated circuits are executed on IBM Quantum, Google Quantum AI (Cirq), or Amazon Braket hardware. We apply error mitigation techniques (zero-noise extrapolation, measurement error mitigation) to improve result quality on NISQ devices and benchmark against classical simulation results.
Step 05 — Hybrid Deployment & Benchmarking
The quantum subroutine is integrated into a classical pipeline — with the classical system handling data encoding, result post-processing, and fallback logic when hardware is unavailable. Comprehensive benchmarking compares the hybrid system against classical baselines across problem size, accuracy, and runtime dimensions.
Real-World Impact
Quantum AI is early-stage — but specific problem classes are already showing compelling quantum advantage over classical methods.
Core Challenge
Classical computers cannot exactly simulate the quantum mechanical behaviour of molecules larger than ~50 atoms — the key bottleneck in computational drug discovery. This forces the use of approximations that miss crucial electron correlation effects, leading to poor binding affinity predictions.
Who Benefits
Pharmaceutical companies, biotech firms, and materials science researchers who need accurate ground-state energy calculations for target molecules — enabling better prediction of drug binding, reaction mechanisms, and material properties without wet-lab synthesis at every iteration.
Request Case StudyCore Challenge
Mean-variance portfolio optimisation with realistic constraints (turnover limits, sector exposure, ESG constraints) is NP-hard as the number of assets grows. Classical heuristics find locally optimal solutions that may leave significant return-per-unit-risk on the table.
Who Benefits
Asset managers, hedge funds, and wealth management platforms that run portfolio optimisation over large asset universes (500+ securities) with complex regulatory constraints — and need solutions that are provably closer to global optima than classical heuristics provide.
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Quantum computers running Shor's algorithm will break RSA and ECC encryption — the backbone of today's internet security — once fault-tolerant quantum hardware matures. Organisations that encrypt today's sensitive data face "harvest now, decrypt later" attacks from adversaries stockpiling ciphertext.
Who Benefits
Defence contractors, financial institutions, and critical infrastructure operators handling data with decade-long sensitivity that need to assess quantum cryptographic risk and begin migrating to NIST-approved post-quantum cryptographic standards before quantum computers reach cryptographically relevant scale.
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Vehicle routing with time windows, capacity constraints, and multi-depot configurations is a computationally hard combinatorial problem. As fleet sizes and delivery point counts grow into the thousands, classical exact solvers fail and heuristics leave 10–20% efficiency on the table.
Who Benefits
Logistics operators, courier networks, and last-mile delivery companies managing large fleets across complex urban networks who need route solutions that are computed faster and are closer to optimal — reducing fuel consumption, delivery windows, and operational costs.
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Leading quantum frameworks, hardware platforms, and classical computing tools — for hybrid quantum-classical development and deployment.
Frequently Asked
What CTOs, research leads, and innovation teams ask before engaging Presear Softwares on quantum AI initiatives.
Ask Our Quantum TeamPartner with Presear Softwares to assess your quantum readiness, prototype hybrid algorithms, and position your organisation at the frontier of quantum-accelerated intelligence.