quchip.devices.transmon

Transmon device models.

  • DuffingTransmon — weakly anharmonic Duffing approximation (valid in the transmon regime \(E_J \gg E_C\); Koch et al. PRA 76, 042319 (2007)).

  • FluxTunableTransmon — SQUID-dispersion flux-tunable transmon (symmetric or asymmetric), suitable for parametric/flux-driven operations and tunable couplers. Inherits from BaseDevice directly; constructor takes physical dressed parameters.

  • ChargeBasisTransmon — exact charge-basis diagonalization; captures charge-dispersion with \(n_g\) outside the deep transmon regime.

class quchip.devices.transmon.ChargeBasisTransmon(E_C, E_J, n_g=0.0, levels=3, label=None, *, num_basis=61, collapse_model='fermi_golden', coupling_channel=None, collapse_rate_threshold=1e-08, **noise_kwargs)[source]

Bases: CircuitDevice

Transmon in the integer charge basis — exact diagonalization.

Parameters:

  • E_C (float) – Charging energy in GHz. Must be positive. May be a JAX tracer.

  • E_J (float) – Josephson energy in GHz. Must be positive. May be a JAX tracer.

  • n_g (float, default 0.0) – Offset charge (units of \(2e\)). May be a JAX tracer.

  • levels (int, default 3) – Truncated eigenbasis size.

  • label (str or None) – Auto-generated as charge_basis_transmon_{n} if omitted.

  • num_basis (int, default 61) – Charge-basis cutoff — must be odd, corresponding to \(n \in [-n_\mathrm{cut}, +n_\mathrm{cut}]\) with \(n_\mathrm{cut} = (\text{num\_basis} - 1)/2\).

  • collapse_model ("fermi_golden" or "ladder", default "fermi_golden") – See CircuitDevice.

  • collapse_rate_threshold (float, default 1e-8) – See CircuitDevice.

  • **noise_kwargs – Forwarded to BaseDevice (T1, T2, thermal_population).

  • coupling_channel (Literal['charge', 'flux'] | None)

Notes

The T1 collapse-operator normalization assumes T1 is set by charge-operator-coupled relaxation (Breuer & Petruccione §3.4). For phonon / quasiparticle / flux-dominated T1, override collapse_operators() or pass collapse_model='ladder'.

tunable_param_names = ('E_C', 'E_J', 'n_g')

Bare parameters this device exposes as differentiable / tunable scalars. fit_a_dress walks this tuple to discover what it is allowed to optimize on each device, decoupling the inverse-design surface from any specific device model. Three states, keyed on whether the value is explicitly declared:

  • No explicit declaration anywhere in the DeviceModel lineage — the default is derived: every declared parameter() field, in declaration order (see DeviceModel.__init_subclass__).

  • Explicit tuple on the class or an ancestor — exact curation, validated at class-definition time; authoritative and inherited until a subclass explicitly replaces it.

  • Explicit empty tuple — deliberately freezes the device (and its subclasses, until one replaces it) out of inverse design.

On a plain (non-DeviceModel) BaseDevice subclass there is no derivation; the default stays empty unless the subclass declares its own tuple — e.g. Fluxonium uses ("E_C", "E_J", "E_L", "phi_ext").

approximation = 'Exact diagonalization in the truncated integer charge basis; accuracy governed by num_basis.'

Declared approximation-regime statement surfaced by physics_notes(), mirroring approximation — this class does not inherit from DeviceModel, so the attribute and its surfacing are declared here directly.

tunable_param_bounds(name, value)[source]

n_g lives in [-0.5, 0.5] (one charge period); other params delegate.

Parameters:
Return type:

tuple[float, float]

physics_notes()[source]

Return declared integer-charge-basis assumptions.

Return type:

list[str]

classmethod from_frequency(freq, anharmonicity, n_g=0.0, levels=3, label=None, *, num_basis=61, collapse_model='fermi_golden', coupling_channel=None, collapse_rate_threshold=1e-08, **noise_kwargs)[source]

Construct from (freq, anharmonicity) using the Koch-regime inversion.

Uses \(E_C = -\alpha\) and \(E_J = (\omega + E_C)^2 / (8 E_C)\). Residual between the Duffing approximation and the exact diagonalized spectrum is typically <1% for \(E_J/E_C > 50\), growing below that. A concrete-scalar warning fires at \(E_J/E_C < 20\).

Parameters:

  • freq (float)

  • anharmonicity (float)

  • n_g (float)

  • levels (int)

  • label (str | None)

  • num_basis (int)

  • collapse_model (Literal['fermi_golden', 'ladder'])

  • coupling_channel (Literal['charge', 'flux'] | None)

  • collapse_rate_threshold (float)

  • noise_kwargs (float | None)

Return type:

ChargeBasisTransmon

property computational: bool

Charge-basis transmon is a computational qubit.

to_dict()[source]

Extend CircuitDevice.to_dict() with the charge-basis circuit parameters.

Return type:

dict[str, Any]

classmethod from_dict(d)[source]

Reconstruct from to_dict() output.

On the registry root, dispatch to the concrete subclass named by data["type"] (forwarding *args / **kwargs). On a concrete subclass, defer to _from_dict_payload(). Concrete subclasses that carry payload override this method directly.

Parameters:

d (dict[str, Any])

Return type:

ChargeBasisTransmon

class quchip.devices.transmon.DuffingTransmon(freq, anharmonicity, *, levels=3, label=None, T1=None, T2=None, thermal_population=None)[source]

Bases: DeviceModel

Transmon modelled as a weakly anharmonic Duffing oscillator.

Parameters:

  • freq (float) – Bare 0 -> 1 transition frequency ω in GHz. Must be positive. May be a JAX tracer for sweeps / gradients.

  • anharmonicity (float) – Anharmonicity α in GHz. Typically negative for superconducting transmons (e.g. -0.25 GHz). May be a JAX tracer.

  • levels (int, default 3) – Fock-space truncation. Three levels suffice for leakage-aware single-qubit modelling; increase for higher-level physics (e.g. iSWAP-family gates via the |02>-|11> crossing).

  • label (str | None, default None) – If omitted, auto-generated as duffing_{idx} via the shared labeling counter.

  • **noise_kwargs – Forwarded to BaseDevice — T1, T2, thermal_population.

Example

>>> from quchip.devices import DuffingTransmon
>>> q = DuffingTransmon(freq=5.0, anharmonicity=-0.25, T1=30_000.0, T2=20_000.0)
>>> len(q.collapse_operators()) >= 1
True
tunable_param_names = ('freq', 'anharmonicity')

Bare parameters this device exposes as differentiable / tunable scalars. fit_a_dress walks this tuple to discover what it is allowed to optimize on each device, decoupling the inverse-design surface from any specific device model. Three states, keyed on whether the value is explicitly declared:

  • No explicit declaration anywhere in the DeviceModel lineage — the default is derived: every declared parameter() field, in declaration order (see DeviceModel.__init_subclass__).

  • Explicit tuple on the class or an ancestor — exact curation, validated at class-definition time; authoritative and inherited until a subclass explicitly replaces it.

  • Explicit empty tuple — deliberately freezes the device (and its subclasses, until one replaces it) out of inverse design.

On a plain (non-DeviceModel) BaseDevice subclass there is no derivation; the default stays empty unless the subclass declares its own tuple — e.g. Fluxonium uses ("E_C", "E_J", "E_L", "phi_ext").

approximation = 'Duffing expansion: cosine Josephson potential truncated at 4th order.'

Declared approximation-regime statement surfaced by physics_notes() — the mechanism that keeps a model’s stated validity range attached to the class rather than buried in a docstring a caller may not read.

computational = True

Whether this device represents a computational qubit, as opposed to e.g. a bus resonator or a coupler element.

freq: Scalar = Parameter(default=<object object>, positive=True, nonnegative=False, serialize=True, unit='GHz')
anharmonicity: Scalar = Parameter(default=<object object>, positive=False, nonnegative=False, serialize=True, unit='GHz')
local_hamiltonian(op)[source]

Return the local Duffing Hamiltonian H = omega n + (alpha/2) n (n - I).

Parameters:

op (LocalOps)

Return type:

PhysicsExpr

physics_notes()[source]

Return declared Duffing-approximation validity notes.

Return type:

list[str]

class quchip.devices.transmon.FluxTunableTransmon(freq, anharmonicity, flux_bias=0.0, asymmetry=0.0, *, levels=3, label=None, T1=None, T2=None, thermal_population=None)[source]

Bases: DeviceModel

SQUID-dispersion flux-tunable transmon.

The constructor takes the calibrated local physical parameters; SQUID metadata is derived on read and is not part of the public interface.

Parameters:

  • freq (float) – Calibrated local 0 -> 1 transition frequency ω in GHz, at the stored flux_bias. Must be positive. May be a JAX tracer.

  • anharmonicity (float) – Calibrated local anharmonicity α in GHz, at the stored flux_bias. Must be negative (α ≈ −E_C). May be a JAX tracer.

  • flux_bias (float, default 0.0) – Calibration-anchor operating point Φ/Φ₀. Any real value; the SQUID inversion is undefined only at the symmetric-SQUID degenerate point (asymmetry == 0 and flux_bias a half-integer — see validate()). The local Hamiltonian does not reference this value directly — freq and anharmonicity already carry it. A pytree leaf, so it is differentiable / sweepable like every other device parameter.

  • asymmetry (float, default 0.0) – SQUID junction asymmetry d = (E_{J1}−E_{J2})/(E_{J1}+E_{J2}). Must be in [0, 1).

  • levels (int, default 3) – Fock-space truncation.

  • label (str | None, default None) – Auto-generated as fluxtunable_{idx} when omitted.

  • **noise_kwargs – Forwarded to BaseDevice — T1, T2, thermal_population.

tunable_param_names = ('freq', 'anharmonicity')

Bare parameters this device exposes as differentiable / tunable scalars. fit_a_dress walks this tuple to discover what it is allowed to optimize on each device, decoupling the inverse-design surface from any specific device model. Three states, keyed on whether the value is explicitly declared:

  • No explicit declaration anywhere in the DeviceModel lineage — the default is derived: every declared parameter() field, in declaration order (see DeviceModel.__init_subclass__).

  • Explicit tuple on the class or an ancestor — exact curation, validated at class-definition time; authoritative and inherited until a subclass explicitly replaces it.

  • Explicit empty tuple — deliberately freezes the device (and its subclasses, until one replaces it) out of inverse design.

On a plain (non-DeviceModel) BaseDevice subclass there is no derivation; the default stays empty unless the subclass declares its own tuple — e.g. Fluxonium uses ("E_C", "E_J", "E_L", "phi_ext").

computational = True

Whether this device represents a computational qubit, as opposed to e.g. a bus resonator or a coupler element.

approximation = 'Duffing-approximated SQUID transmon; adiabatic flux (calibration-anchor, no Landau-Zener).'

Declared approximation-regime statement surfaced by physics_notes() — the mechanism that keeps a model’s stated validity range attached to the class rather than buried in a docstring a caller may not read.

freq: Scalar = Parameter(default=<object object>, positive=True, nonnegative=False, serialize=True, unit='GHz')
anharmonicity: Scalar = Parameter(default=<object object>, positive=False, nonnegative=False, serialize=True, unit='GHz')
flux_bias: Scalar = Parameter(default=0.0, positive=False, nonnegative=False, serialize=True, unit='Phi_0')
asymmetry: Scalar = Parameter(default=0.0, positive=False, nonnegative=False, serialize=True, unit=None)
validate()[source]

Range checks on concrete scalars only; traced values pass unchecked.

Return type:

None

local_hamiltonian(op)[source]

Return the Duffing Hamiltonian built from the calibrated freq and anharmonicity.

H = ω n + (α/2) n(n − I). Does not reference flux_bias.

Parameters:

op (LocalOps)

Return type:

PhysicsExpr

frequency_at(flux)[source]

SQUID dispersion ω(Φ/Φ₀) in GHz, using derived E_C and E_J_max.

Parameters:

flux (float) – Reduced flux Φ/Φ₀. JAX-traceable.

Return type:

Any

flux_for_frequency(target_freq)[source]

Inverse SQUID dispersion on the monotonic lobe Φ/Φ₀ ∈ [0, 0.5).

Derivation:

ω(Φ) = sqrt(8 E_C E_J_max sqrt(cos²(πΦ) + d²sin²(πΦ))) − E_C → let S = (ω + E_C)² / (8 E_C E_J_max) → cos²(πΦ)(1 − d²) + d² = S² → cos²(πΦ) = (S² − d²) / (1 − d²)

Raises:

ValueError – If target_freq is concrete and lands outside the frequency range frequency_at() reaches over Φ/Φ₀ ∈ [0, 0.5) at the current calibration anchor. A traced target_freq (or a traced anchor) skips this check; the returned flux clips to the lobe endpoint, so out-of-domain behavior is undefined for traced inputs.

Parameters:

target_freq (Any)

Return type:

Any

physics_notes()[source]

Return declared SQUID-transmon calibration-anchor assumptions.

Return type:

list[str]

Modules

charge_basis

Charge-basis transmon — circuit-level transmon in the integer charge basis.

duffing

Duffing-approximation transmon qubit.

flux_tunable

Symmetric/asymmetric-SQUID flux-tunable transmon.