The state of quantum hardware for molecular simulation
The variational quantum eigensolver, or VQE, is one of the most studied near term approaches to quantum chemistry. Introduced by Peruzzo and colleagues in 20141, it pairs a short quantum circuit, which prepares a trial state of a molecule’s electrons, with a classical optimiser that adjusts the circuit until the estimated energy stops falling. Molecular ground state energies fit this structure naturally.
The limit is hardware noise. Preskill’s widely cited description of the noisy intermediate scale quantum, or NISQ, era makes the point that noise in quantum gates limits the size of circuits that can be run reliably2, and later work shows that noise can also make variational circuits harder to train3. A 2026 Nature Biotechnology editorial reaches the same place for biology: quantum computers are not yet able to address biological challenges independently and at scale4. For molecules that matter to tissue engineering, such as extracellular matrix proteins and growth factor receptors, current devices are proofs of concept rather than production tools.
The honest position is that we are in the simulation of simulation phase: demonstrating on quantum hardware results that classical hardware could produce, but building the pipeline that will matter when the hardware improves.
Slide the circuit angle, or let the classical optimiser walk downhill. A teaching sketch of the variational idea, not a real molecule.
Tissue engineering as a target domain
Tissue engineering leans on molecular level questions: how scaffold materials interact with signalling proteins, how stable bioactive peptides are under physiological conditions, and how bioresorbable polymers degrade. Reviews of quantum computing for drug discovery describe electronic structure problems of this kind as the most plausible early target5.
Where we think the nearest targets sit
The list below is our own assessment, not a finding from the literature, and we have not run any of these on hardware:
- Surface binding energy calculations for small peptide sequences on polymer substrates
- Conformational stability analysis for short bioactive motifs under varying pH conditions
- Ground state energy estimation for model systems representing degradable linkage chemistries
Where the research frontier is moving
IBM has published a roadmap that targets a large scale, fault tolerant quantum computer by 20296. A roadmap is a target, not a result, and Preskill’s caution that a 100 qubit machine will not change the world right away2 still applies. The latest hybrid demonstrations, covered in our July Market Intelligence note, scale by splitting a problem between classical supercomputers and quantum processors, rather than asking the quantum hardware to do all the work.
What this means for how we build
Burgeon Studio is an AI native software company. For most problems in life sciences, AI and classical computing are the right tools, and we reach for them first. We add quantum methods only where the data complexity, the size of the search or the electronic structure of a problem is more than AI can handle. Today that is a narrow set of problems, and this paper is the reason we keep it narrow.
- Peruzzo et al., “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications 5, 4213 (2014).
- Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum 2, 79 (2018).
- Wang et al., “Noise induced barren plateaus in variational quantum algorithms,” Nature Communications (2021).
- Nature Biotechnology editorial, “Quantum computing in transition,” volume 44, pages 1065 to 1066 (6 July 2026).
- Blunt et al., “Perspective on the current state of the art of quantum computing for drug discovery applications,” Journal of Chemical Theory and Computation (2022).
- IBM quantum roadmap through 2029, summarised by PostQuantum.
VQE stands for variational quantum eigensolver, a hybrid quantum classical algorithm. Statements about our own approach are Burgeon Studio’s view.