Quantum Computing & R&D
Quantum computing uses quantum mechanical effects to run certain calculations differently from a classical computer, and today it is used for research and benchmarking rather than production workloads.
We help R&D-led teams find out whether quantum computing can help them, with algorithm prototyping, hybrid classical-quantum workflows, and advisory across Qiskit, Cirq, and Braket. The deliverable is a reproducible benchmark against a strong classical baseline, not a press release.
Who this is for
- R&D or innovation teams with a specific hard optimisation, simulation, or machine learning problem
- Organisations that need an evidence-based answer on quantum readiness rather than vendor material
- Research groups wanting a hybrid pipeline built and benchmarked properly
- Engineering teams who want to be able to run quantum experiments themselves afterwards
When it's not a fit
- You are looking for a production speed-up today. For almost every commercial workload, classical computing is still the right answer and we will tell you that early.
- There is no specific problem yet, only an interest in the field. Without a candidate problem there is nothing to benchmark.
- The goal is the announcement rather than the result. We publish honest findings, including negative ones.
- Nobody internally can maintain the work afterwards. A prototype with no owner stops being useful the moment we leave.
What We Deliver
Quantum Algorithm Prototyping
Design and benchmark candidate algorithms — VQE, QAOA, amplitude estimation, quantum ML — against classical baselines.
Hybrid Classical-Quantum Pipelines
Pipelines that orchestrate classical pre- and post-processing with quantum kernels on real and simulated backends.
Quantum ML Experiments
Variational quantum classifiers and kernel methods integrated with PyTorch and TensorFlow workflows.
Quantum Optimisation
Mapping combinatorial problems to QUBO and Ising formulations and evaluating QAOA-style approaches against classical solvers.
Advisory & Roadmaps
Readiness reviews, vendor selection across IBM, AWS, IonQ, and Quantinuum, and a buildable twelve to eighteen month roadmap.
Education & Enablement
Workshops and internal enablement so your engineering team can own quantum experiments going forward.
Common Use Cases
- Proof-of-concept work for R&D and innovation teams
- Optimisation experiments in logistics, scheduling, and portfolio construction
- Quantum machine learning research collaborations
- Hybrid pipelines benchmarking quantum against classical baselines
- Internal upskilling on Qiskit, Cirq, and quantum hardware access
- Vendor and hardware evaluation for enterprise pilots
Tech Stack
Outcomes you can expect
- A clear, honest read on where quantum can and cannot help you today
- Reproducible benchmarks rather than vendor claims
- A buildable path from prototype to a production pilot, if one exists
- An in-house team able to run the next experiment without us
What you receive
Everything below is handed over to you. Code and infrastructure live in your accounts, not ours.
- A written problem formulation, including why it is or is not a quantum candidate
- Prototype code in a repository, runnable on a simulator without hardware access
- Benchmark results against a strong classical baseline, with the baseline code included
- A reproducibility note: versions, seeds, backends, and how to re-run everything
- A roadmap covering the next twelve to eighteen months
- A workshop for your team on the code and the reasoning behind it
Engagement models
Fixed-scope project
A defined deliverable at a fixed price, scoped up front. Best when you know what you need built and want a firm schedule and budget.
Best when: You know what you need built and want a firm budget and date.
Retainer
Ongoing delivery, maintenance, or advisory billed monthly. Best for continuous improvement after launch, or when priorities shift faster than a fixed scope allows.
Best when: The work is continuous and priorities shift faster than a fixed scope allows.
Extended team
Our engineers working inside your team, your process, and your repositories. Best when you have the roadmap but not the capacity or the specific skills.
Best when: You have the roadmap but not the capacity or a specific skill.
Frequently asked questions about Quantum Computing & R&D
Very little in production, and we will say so plainly. What it can do is de-risk the next few years: establish whether your optimisation or simulation problems map onto quantum algorithms at all, and give you a reproducible answer instead of vendor claims.
A pipeline where classical computing does most of the work and a quantum processor handles one specific step. Classical code prepares the data and post-processes the results; the quantum kernel runs the part where an advantage might exist. Every practical quantum application today is built this way.
Qiskit, Cirq, and PennyLane for algorithms, and AWS Braket for hardware access, alongside IBM Quantum, IonQ, and Quantinuum backends. Experiments run on simulators first and on real hardware when the problem size justifies it. Results integrate with Python, NumPy, and PyTorch workflows.
We take one candidate problem, formulate it for an algorithm such as VQE or QAOA, and benchmark it against a strong classical baseline. You get reproducible results, an honest read on whether quantum helps, and a roadmap covering the next twelve to eighteen months.
No. Quantum processors are accessed as a cloud service through providers such as AWS Braket and IBM Quantum, and most experiments run on simulators first. Choosing which backend suits your problem is part of the engagement, so you are not committing to one vendor early.
Teams with an R&D or innovation function and a specific hard problem — optimisation, simulation, or machine learning — who want evidence rather than a press release. If you have no such problem yet, the honest answer is that quantum work is premature for you.
Discovery. We agree the goal, review whatever exists already, and produce a written scope: what will be built, in what order, what you will receive, and what we need from you. It ends with a plan you can approve, amend, or take elsewhere.
The outcome you want and how you will judge it, access to any existing code, designs, hosting, and analytics, one person who can make decisions, and any hard constraints — a fixed launch date, a required stack, compliance obligations. Missing pieces we can work around; an unnamed decision-maker we cannot.
Both. Our published work spans early-stage products and established companies, and our smallest engagements are single fixed-scope projects. What matters is that someone on your side can make decisions and that the problem is defined well enough to scope, not company size.
Yes, as a retainer covering monitoring, security patching, dependency upgrades, performance work, and a monthly allowance for fixes and small changes. You get a report each month showing what was done. It is a separate engagement from the build, so upkeep does not compete with feature work.
Engineering and design work is delivered remotely, so location is not a constraint for those. Our in-person work — hackathons, builder houses, developer meetups, and campus programmes — runs in Delhi, Bangalore, Hyderabad, and Mumbai, plus university campuses across the country.
You get a written summary of what we understood, a proposed scope broken into phases, what each phase delivers, and the engagement model we would recommend. If we think you should buy something off the shelf or not build yet, the summary says that instead.